Complex real-world processes are distinguished by various properties which make control or optimization more difficult. The processes are, first of all, very complex solely on account of the number of available measured, controlled and manipulated variables. Furthermore, these processes are usually time-variant, i.e. external and internal influences (seasons, material qualities, operating states) give rise to relationships which change over time in the process data. A control system therefore needs to be adaptive and needs to be constantly adjusted.
In a control system which is known from EP 1 396 770 B1, process models of the controlled system (simulators) are developed in the background and trained. If the best new process model provides a higher level of accuracy for the forecasts than the process model used in the active controller, the latter is replaced by the best new process model, which gives rise to a new active controller. Then, the previously used process model and the other new process models are discarded.